class_docstr: First version of Epistemic computation used as active learning strategy
class_docstr_tok: ["First","version","of","Epistemic","computation","used","as","active","learning","strategy"]
code: def __init__(
      self,
      infer_task: BasicInferTask,
      max_samples=0,
      simulation_size=5,
      use_variance=False,
      key_output_entropy="epistemic_entropy",
      key_output_ts="epistemic_ts",
  ):
      super().__init__(
          f"Compute initial score based on dropout - {infer_task.description}"
      )
      self.infer_task = infer_task
      self.dimension = infer_task.dimension
  
      self.max_samples = max_samples
      self.simulation_size = simulation_size
      self.use_variance = use_variance
      self.key_output_entropy = key_output_entropy
      self.key_output_ts = key_output_ts
  
file_path: MONAILabel/monailabel/tasks/scoring/epistemic_v2.py
imports: import logging
  import multiprocessing
  import os
  import time
  from concurrent.futures import ThreadPoolExecutor
  import numpy
  import torch
  from monai.metrics.active_learning_metrics import VarianceMetric
  from monailabel.interfaces.datastore import Datastore
  from monailabel.interfaces.tasks.scoring import ScoringMethod
  from monailabel.tasks.infer.basic_infer import BasicInferTask
  from monailabel.utils.others.generic import name_to_device
label_desc: Use image node_id 1 to create a new EpistemicScoring object from inherited base classes: ScoringMethod with example: obj = EpistemicScoring(infer_task, max_samples, simulation_size, use_variance, key_output_entropy, key_output_ts)